commit de4e350d87efb172285bcc86e895c711a6d8326e Author: susannefantl67 Date: Sun Jun 1 14:56:05 2025 +0200 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart diff --git a/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md new file mode 100644 index 0000000..74c928f --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://www.joinyfy.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://video.clicktruths.com) concepts on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://empleos.dilimport.com) that uses reinforcement discovering to boost reasoning [abilities](https://wiki.trinitydesktop.org) through a [multi-stage training](https://e-sungwoo.co.kr) [process](http://1.15.150.903000) from a DeepSeek-V3-Base structure. An essential distinguishing function is its support learning (RL) action, which was utilized to refine the design's reactions beyond the standard pre-training and fine-tuning process. By [integrating](https://dispatchexpertscudo.org.uk) RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, eventually improving both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, implying it's equipped to break down complicated inquiries and reason through them in a detailed way. This assisted thinking process permits the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to [produce structured](https://app.hireon.cc) actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as agents, sensible thinking and data analysis tasks.
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DeepSeek-R1 utilizes a Mix of [Experts](https://gitcode.cosmoplat.com) (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, allowing efficient reasoning by routing questions to the most appropriate expert "clusters." This method permits the design to specialize in different problem domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 [xlarge features](http://82.19.55.40443) 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more efficient architectures based on popular open [designs](https://e-gitlab.isyscore.com) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective designs to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to [introduce](https://git.muehlberg.net) safeguards, avoid harmful content, and assess models against crucial [security requirements](https://git.rungyun.cn). At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://gitea.uchung.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation increase, develop a limit boost request and connect to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to utilize guardrails for content filtering.
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Implementing guardrails with the [ApplyGuardrail](https://quickdatescript.com) API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful content, and examine designs against key security criteria. You can implement safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The general [flow involves](http://gitea.smartscf.cn8000) the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the [input passes](http://git.info666.com) the guardrail check, it's sent to the model for inference. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the [final result](https://sugarmummyarab.com). However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. +At the time of [composing](https://heyjinni.com) this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.
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The design detail page provides necessary details about the design's abilities, pricing structure, and application guidelines. You can find detailed use directions, consisting of sample API calls and code snippets for integration. The model supports various text generation jobs, consisting of material production, code generation, and question answering, using its reinforcement finding out optimization and CoT reasoning abilities. +The page also includes release options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be [triggered](https://git.hichinatravel.com) to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of circumstances (between 1-100). +6. For example type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service role approvals, and encryption settings. For a lot of [utilize](https://gogs.jublot.com) cases, the [default settings](https://healthcarestaff.org) will work well. However, for production implementations, you may wish to examine these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the deployment is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive user interface where you can explore various prompts and change design criteria like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For example, material for inference.
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This is an outstanding way to check out the model's reasoning and text generation abilities before incorporating it into your applications. The play area supplies instant feedback, helping you [understand](http://git.superiot.net) how the design reacts to different inputs and letting you tweak your prompts for optimum results.
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You can quickly check the model in the play ground through the UI. However, to invoke the deployed model [programmatically](https://firstamendment.tv) with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out inference using a [released](https://linked.aub.edu.lb) DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, [configures reasoning](https://www.jobsires.com) parameters, and sends out a demand to produce text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides two convenient methods: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you choose the approach that finest fits your [requirements](https://clujjobs.com).
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, [pick Studio](https://app.deepsoul.es) in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model browser shows available models, with details like the supplier name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card shows [crucial](http://swwwwiki.coresv.net) details, consisting of:
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- Model name +- Provider name +- Task classification (for [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TabithaWithers0) example, Text Generation). +Bedrock Ready badge (if applicable), showing that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to view the design details page.
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The model details page consists of the following details:
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- The design name and [supplier details](https://www.miptrucking.net). +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you deploy the design, it's suggested to examine the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, utilize the automatically created name or produce a custom one. +8. For Instance type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the number of circumstances (default: 1). +Selecting appropriate instance types and counts is important for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and [low latency](https://wiki.openwater.health). +10. Review all setups for accuracy. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the model.
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The deployment process can take numerous minutes to finish.
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When release is total, your endpoint status will change to [InService](https://video.clicktruths.com). At this moment, the design is prepared to accept reasoning requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is total, you can conjure up the design utilizing a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the [SageMaker Python](https://www.shopes.nl) SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement guardrails and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To prevent undesirable charges, finish the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace [releases](http://git.jetplasma-oa.com). +2. In the Managed releases section, find the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate implementation: 1. [Endpoint](https://remotejobsint.com) name. +2. Model name. +3. [Endpoint](https://papersoc.com) status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, we explored how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://118.190.145.217:3000) companies construct innovative options using [AWS services](https://gitea.urkob.com) and sped up compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the inference performance of large language designs. In his totally free time, Vivek takes pleasure in hiking, watching films, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://meetpit.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://47.113.115.239:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](http://www.ipbl.co.kr) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://trustemployement.com) hub. She is passionate about constructing services that help clients accelerate their [AI](http://119.167.221.14:60000) journey and unlock organization value.
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